摘要
In this paper, we propose several improved neural networks and training strategy using data augmentation to segment human radius accurately and efficiently. This method can provide pixel-level segmentation accuracy through the low-level features of the neural network, and automatically distinguish the classification of radius. The versatility and applicability can be effectively improved by learning and training digital X-ray images obtained from digital X-ray imaging systems of different manufacturers.
In this paper, we propose several improved neural networks and training strategy using data augmentation to segment human radius accurately and efficiently. This method can provide pixel-level segmentation accuracy through the low-level features of the neural network, and automatically distinguish the classification of radius. The versatility and applicability can be effectively improved by learning and training digital X-ray images obtained from digital X-ray imaging systems of different manufacturers.
作者
Songzheng Huang
Jianfeng Chen
Songzheng Huang;Jianfeng Chen(Zhejiang Kangyuan Medical Devices Incorporation, Hangzhou, China;Department of Radiology and Medical Imaging, Stritch School of Medicine, Loyola University Medical Center, Chicago, USA)
